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We propose a general framework for denoising high-dimensional measurements which requires no prior on the signal, no estimate of the noise, and no clean training data.
Memoires associatives distribuees: Une comparaison (Distributed associative memories: A comparison)
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JPEG still image data compression standard
Pennebaker, W. B. and Mitchell, J. L · 1992
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Adaptive wavelet thresholding for image denoising and compression
Chang, S. G., Yu, B., and Vetterli, M · 2000
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A non-local algorithm for image denoising
Buades, A., Coll, B., and Morel, J.-M · 2005
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Image denoising via sparse and redundant representations over learned dictionaries
Elad, M. and Aharon, M · 2006
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Image denoising by sparse 3-D transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K · 2007
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Cross-validation of component models: A critical look at current methods
Bro, R., Kjeldahl, K., Smilde, A. K., and Kiers, H. A. L · 2008
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Bi-cross-validation of the SVD and the nonnegative matrix factorization
Owen, A. B. and Perry, P. O · 2009
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BioNumbers – the database of key numbers in molecular and cell biology
Milo, R., Jorgensen, P., Moran, U., Weber, G., and Springer, M · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A · 2010
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An analysis and implementation of the BM3D image denoising method
Lebrun, M · 2012
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Annotated high-throughput microscopy image sets for validation
Ljosa, V., Sokolnicki, K. L., and Carpenter, A. E · 2012
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Machine Learning: a Probabilistic Perspective
Murphy, K. P · 2012
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Annotated high-throughput microscopy image sets for validation
Ljosa, V., Sokolnicki, K. L., and Carpenter, A. E · 2012
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Machine Learning: a Probabilistic Perspective
Murphy, K. P · 2012
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scikit-image: image processing in Python
van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., Yu, T., and contributors, t. s.-i · 2014
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scikit-image: image processing in Python
van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., Yu, T., and contributors, t. s.-i · 2014
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Transcriptional heterogeneity and lineage commitment in myeloid progenitors
Paul, F., Arkin, Y., Giladi, A., Jaitin, D., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., David, E., Cohen, N., Lauridsen, F., Haas, S., Schlitzer, A., Mildner, A., Ginhoux, F., Jung, S., Trumpp, A., Porse, B., Tanay, A., and Amit, I · 2015
Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising
Zhang, K., Zuo, W., Chen, Y., Meng, D., and Zhang, L · 2017
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Automatic differentiation in PyTorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Model-blind video denoising via frame-to-frame training
Ehret, T., Davy, A., Facciolo, G., Morel, J.-M., and Arias, P · 2018
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Noise2Void - learning denoising from single noisy images
Krull, A., Buchholz, T.-O., and Jug, F · 2018
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Noise2Noise: Learning image restoration without clean data
Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M., and Aila, T · 2018
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U-Net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Transcriptional heterogeneity and lineage commitment in myeloid progenitors
Paul, F., Arkin, Y., Giladi, A., Jaitin, D., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., David, E., Cohen, N., Lauridsen, F., Haas, S., Schlitzer, A., Mildner, A., Ginhoux, F., Jung, S., Trumpp, A., Porse, B., Tanay, A., and Amit, I · 2015
Cited alongside, same era.
U-Net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Bi-cross-validation for factor analysis
Owen, A. B. and Wang, J · 2016
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Convolutional dictionary learning via local processing
Papyan, V., Romano, Y., Sulam, J., and Elad, M · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Cited alongside, same era.
Metzler, C. A., Mousavi, A., Heckel, R., and Baraniuk, R. G · 2018
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Correction by projection: Denoising images with generative adversarial networks
Tripathi, S., Lipton, Z. C., and Nguyen, T. Q · 2018
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Recovering gene interactions from single-cell data using data diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., Bierie, B., Mazutis, L., Wolf, G., Krishnaswamy, S., and Pe’er, D · 2018
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Content-Aware image restoration: Pushing the limits of fluorescence microscopy
Weigert, M., Schmidt, U., Boothe, T., Müller, A., Dibrov, A., Jain, A., Wilhelm, B., Schmidt, D., Broaddus, C., Culley, S., Rocha-Martins, M., Segovia-Miranda, F., Norden, C., Henriques, R., Zerial, M., Solimena, M., Rink, J., Tomancak, P., Royer, L., Jug, F., and Myers, E. W · 2018
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Zhussip, M., Soltanayev, S., and Chun, S. Y · 2018
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Noise2Void - learning denoising from single noisy images
Krull, A., Buchholz, T.-O., and Jug, F · 2018
Later among the works it cites.
Recovering gene interactions from single-cell data using data diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., Bierie, B., Mazutis, L., Wolf, G., Krishnaswamy, S., and Pe’er, D · 2018
Later among the works it cites.